Capacity configuration method and system of energy storage in microgrid

ABSTRACT

A capacity configuration method and system of energy storage in a microgrid. In this application, the time-series data related to photovoltaic power generation is acquired and processed to obtain the preprocessed time-series data; a time-series generative adversarial network (Time GAN) is trained based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; and based on the enhanced time-series data, a distributionally robust optimization model is used to perform capacity configuration of energy storage.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority from Chinese PatentApplication No. 202210487370.7, filed on May 6, 2022. The content of theaforementioned application, including any intervening amendmentsthereto, is incorporated herein by reference in its entirety.

TECHNICAL FIELD

This application relates to energy storage capacity configuration, andmore particularly to a capacity configuration method and system ofenergy storage in a microgrid.

BACKGROUND

Considering that the energy storage system struggles with the high costper unit capacity, expensive investment cost and high operation andmaintenance cost, it is restricted for the energy storage system to bewidely promoted and applied in the microgrid. For that reason, it isneeded to take effective methods to optimize the capacity configurationof the energy storage system. As a typical problem, the capacityoptimization of the energy storage equipment requires correspondinghistorical data as support in the solving process. However, the capacityof the energy storage equipment is required to be planned in the earlystage of the microgrid construction, but the data of the microgrid atthat time is insufficient. Under such circumstance, it is important tomanage the uncertainty of photovoltaic power generation and plan forcapacity configuration of the energy storage.

At present, three main methods for managing the uncertainty ofphotovoltaic power generation include stochastic optimization, robustoptimization and distributionally robust optimization. In the stochasticoptimization, it is generally assumed that the photovoltaic power obeysa given probability distribution, so as to model the uncertainties. Inthe robust optimization, an uncertainty set is applied to represent thevariation range of the photovoltaic power, and a satisfactory solutionwith good performance feasible in all uncertainties is sought. In thedistributionally robust optimization, the advantages of the stochasticoptimization and the advantages of the stochastic optimization arecombined. Based on the uncertainties in the probability distributionfunction, the probability distribution of the photovoltaic outputscenario under the worst-case scenario is sought to describe theuncertainty of photovoltaic output.

However, in the traditional stochastic optimization, it is difficult forthe existing probability distribution function to accurately describethe fluctuation of photovoltaic power generation. In addition, thetraditional robust optimization directly makes decisions according tothe worst-case scenario in the uncertainty set, making the optimizationsolution more conservative. Although combined the strengths of thestochastic optimization and the robust optimization, thedistributionally robust optimization requires a large amount of datawhen solving and it is hard for some newly-built microgrids to acquiresufficient data to satisfy the conditions. Hence, the effective methodfor accurately configuring the energy storage capacity in thenewly-built microgrids needs to be developed.

SUMMARY

An objective of this application is to provide a capacity configurationmethod and system of energy storage in a microgrid to overcome theinaccurate capacity configuration of energy storage in the newly-builtmicrogrids.

In order to achieve those objectives, technical solutions of thisapplication are described as follows.

In a first aspect, this application provides a capacity configurationmethod of energy storage in a microgrid, comprising:

acquiring time-series data related to photovoltaic power generation, andperforming preprocessing of the time-series data related to thephotovoltaic power generation to obtain preprocessed time-series data;training a time-series generative adversarial network (Time GAN) basedon the preprocessed time-series data to perform data enhancement toobtain enhanced time-series data; wherein the Time GAN comprises anembedded network and a generative adversarial network (GAN); and

based on the enhanced time-series data, using a distributionally robustoptimization model to perform capacity configuration of energy storage.

In an embodiment, the time-series data related to the photovoltaic powergeneration comprises photovoltaic power data, global horizontalradiation and diffuse horizontal radiation data, temperature data, andhumidity data; the preprocessing comprises data cleaning processing,data integration processing, data transformation processing, datareduction processing, and data standardization processing.

In an embodiment, the Time GAN comprises an embedded network and agenerative adversarial network (GAN); and the step of “training atime-series generative adversarial network (Time GAN) based on thepreprocessed time-series data to perform data enhancement to obtainenhanced time-series data” comprises:

(S21) training the embedded network based on the time-series data; andthe embedded network is formed by embedding a function used fordimensionality reduction of the time-series data into an autoencoder;

(S22) training a generator and a discriminator in the GAN based on thetime-series data; and

(S23) performing the data enhancement on the time-series data viajoint-training of the embedded network and the GAN.

In an embodiment, the distributionally robust optimization modelcomprises:

an objective function of the distributionally robust optimization modelis expressed as follows:

${{C = {C_{1} + C_{2}}};}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};}$${C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};$

where C is the total investment cost of energy storage; C₁ indicates thedaily average investment cost of energy storage battery; C₂ indicatesthe daily operating cost; r_(e) indicates the fund recovery factor; C′₁indicates the investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesthe depth of discharge of the energy storage battery; d indicates thediscount rate; y indicates the investment life of the energy storagebattery; p_(g,t) indicates the price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesthe price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate the charging and discharging power of energy storage at momentt, respectively; and C_(ess) indicates the cost per unit charge anddischarge of the energy storage battery;

constraints of the objective function comprise:

a. an operation constraint of energy storage equipment is expressed asfollows:

$\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right.$

where SOC_(t) is the capacity of the battery at time t; φ is theself-discharging rate of the battery; η is the charging efficiency ofthe battery; p_(t) ^(c) is the charging power of the battery at time t;p_(t) ^(d) is the discharging power of the battery at time t; p_(min)^(c) is the minimum charging power of the battery; p_(max) ^(c) is themaximum charging power of the battery; p_(min) ^(c) is the minimumdischarging power of the battery; p_(max) ^(d) is the maximumdischarging power of the battery; and Δt is dispatching time interval;

b. an operation constraint of a gas turbine is expressed as follows:

$\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leq P_{{gt},t} \leq P_{gt}^{\max}}\end{matrix};} \right.$

where V_(chp,t) is the amount of natural gas purchased at time t; ω isthe electrical efficiency of the gas turbine; J is the heat value of thenatural gas; H_(gt,t) is the thermal power output by the gas turbine attime t; P_(gt) ^(min) is the upper limit of electrical power of the gasturbine; and P_(gt) ^(max) is the lower limit of the electrical power ofthe gas turbine.

c. a constraint of power balance is expressed as follows:

$\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right.$

where P_(grid,t) represents the power transmission between the microgridand the main grid at time t; P_(pv,t) is the power output of thephotovoltaic power generation at time t; p_(t) ^(c) is the chargingpower of the energy storage battery at time t; p_(t) ^(d) is thedischarging power of the of the energy storage battery at time t,P_(load,t) is the total electrical load demand of the microgrid at timet; H_(gt,t) is the thermal power output of the gas turbine at time t;and H_(load,t) is the total heat load demand of the microgrid at time t;and

an ambiguity set M^(ε) used for measuring an uncertainty of thedistributionally robust optimization model is shown as follows:

M ^(ε={P) _({circumflex over (p)}) ∈ M(ξ):d _(W)(P _({tilde over (p)}) ,{circumflex over (P)})≤ε};

where P_({circumflex over (p)}) is the probability distribution of theactual output power of the photovoltaic power generation; {circumflexover (P)} is the empirical distribution of the photovoltaic powergeneration; M(ξ) is all probability distribution spaces defined byWasserstein distance d_(W); and ε is radius of the ambiguity set W.

In an embodiment, the step of “using a distributionally robustoptimization model to perform capacity configuration of energy storage”comprises:

solving the distributionally robust optimization model by a commercialsolver to obtain a solution result; and performing the capacityconfiguration of energy storage based on the solution result.

In a second aspect, this application provides a capacity configurationsystem of energy storage in a microgrid, comprising:

a data acquisition and preprocessing module;

a time-series generative adversarial network (Time GAN) photovoltaicpower generation scenario generation module; and

an energy storage capacity optimization and output module;

wherein the data acquisition and preprocessing module is configured toacquire time-series data related to photovoltaic power generation andpreprocess the time-series data related to the photovoltaic powergeneration to obtain preprocessed time-series data;

the time-series generative adversarial network (Time GAN) photovoltaicpower generation scenario generation module is configured to train theTime GAN to perform data enhancement based on the preprocessedtime-series data to obtain enhanced time-series data; the Time GANcomprises an embedded network and a generative adversarial network(GAN); and

the energy storage capacity optimization and output module is configuredto use the distributionally robust optimization model to performcapacity configuration of energy storage based on the enhancedtime-series data.

In an embodiment, the time-series data related to the photovoltaic powergeneration comprises photovoltaic power data, global horizontalradiation and diffuse horizontal radiation data, temperature data, andhumidity data; the preprocessing comprises data cleaning processing,data integration processing, data transformation processing, datareduction processing, and data standardization processing.

In an embodiment, the time-series generative adversarial network (TimeGAN) photovoltaic power generation scenario generation module isconfigured to train the Time GAN to perform data enhancement based onthe preprocessed time-series data to obtain enhanced time-series data;the Time GAN comprises an embedded network and a generative adversarialnetwork (GAN); the time-series generative adversarial network (Time GAN)photovoltaic power generation scenario generation module is operatedthrough steps of:

(S21) training the embedded network based on the time-series data; andthe embedded network is formed by embedding a function used fordimensionality reduction of the time-series data into an autoencoder;

(S22) training a generator and a discriminator in the GAN based on thetime-series data; and

(S23) performing the data enhancement on the time-series data viajoint-training of the embedded network and the GAN.

In an embodiment, the distributionally robust optimization modelcomprises:

an objective function of the distributionally robust optimization modelis expressed as follows:

${{C = {C_{1} + C_{2}}};}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};}$${C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};$

where C is the total investment cost of energy storage; C₁ indicates thedaily average investment cost of energy storage battery; C₂ indicatesthe daily operating cost; r_(e) indicates the fund recovery factor; C′₁indicates the investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesthe depth of discharge of the energy storage battery; d indicates thediscount rate; y indicates the investment life of the energy storagebattery; p_(g,t) indicates the price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesthe price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate the charging and discharging power of energy storage at momentt, respectively; C_(e)indicates the cost per unit charge and dischargeof the energy storage battery;

constraints of the objective function comprise:

a. an operation constraint of energy storage equipment is expressed asfollows:

$\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right.$

where SOC_(t) is the capacity of the battery at time t; φ is theself-discharging rate of the battery; η is the charging efficiency ofthe battery; p_(t) ^(c) is the charging power of the battery at time t;p_(t) ^(d) is the discharging power of the battery at time t; p_(min)^(c) is the minimum charging power of the battery; p_(max) ^(c) is themaximum charging power of the battery; p_(min) ^(d) is the minimumdischarging power of the battery; p_(max) ^(d) is the maximumdischarging power of the battery; and Δt is dispatching time interval;

b. an operation constraint of a gas turbine is expressed as follows:

$\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leq P_{{gt},t} \leq P_{gt}^{\max}}\end{matrix};} \right.$

where V_(chp,t) is the amount of natural gas purchased at time t; ω isthe electrical efficiency of the gas turbine; J is the heat value of thenatural gas; H_(gt,t) is the thermal power output by the gas turbine attime t; P_(gt) ^(min) is the upper limit of electrical power of the gasturbine; and P_(gt) ^(max) is the lower limit of the electrical power ofthe gas turbine; and c. a constraint of power balance is expressed asfollows:

$\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right.$

where P_(grld,t) represents the power transmission between the microgridand the main grid at time t; p_(t) ^(d) is the power output of thephotovoltaic power generation at time t; p_(t) ^(c) is the chargingpower of the energy storage battery at time t; P_(d,t) is thedischarging power of the of the energy storage battery at time t,P_(load,t) is the total electrical load demand of the microgrid at timet; H_(gt,t) is the thermal power output of the gas turbine at time t;and H_(load,t) is the total heat load demand of the microgrid at time t;and

an ambiguity set M^(ε) used for measuring an uncertainty of thedistributionally robust optimization model is shown as follows:

M ^(ε) ={P _({circumflex over (p)}) ∈ M(ξ):d _(W)(P _({tilde over (p)}), {circumflex over (P)})≤ε};

where P_({circumflex over (p)}) is the probability distribution of theactual output power of the photovoltaic power generation; P is theempirical distribution of the photovoltaic power generation; M(ξ) is allprobability distribution spaces defined by Wasserstein distance d_(W);and ε is radius of the ambiguity set W.

In an embodiment, the distributionally robust optimization model isconfigured to perform the capacity configuration of energy storage,which is operated through a step of:

solving the distributionally robust optimization model by a commercialsolver to obtain a solution result; and performing the capacityconfiguration of energy storage based on the solution result.

Compared with the prior art, this application has the followingbeneficial effects.

1. With regard to the method and the system provided herein, thetime-series data related to photovoltaic power generation is acquiredand processed to obtain the preprocessed time-series data; a time-seriesgenerative adversarial network (Time GAN) is trained based on thepreprocessed time-series data to perform data enhancement to obtainenhanced time-series data; and based on the enhanced time-series data, adistributionally robust optimization model is used to perform capacityconfiguration of energy storage. The Time GAN is configured to performdata enhancement on the limited data obtained by the newly-builtmicrogrid, so as to allow the energy storage capacity of the newly-builtmicrogrid to be accurately configured using the distributionally robustoptimization model when the data is extremely insufficient, improvingthe utilization efficiency of the data and the construction efficiencyof the microgrid.

2. Compared with ordinary generative adversarial network (GAN), the TimeGAN provided herein is configured to perform data enhancement, whichconsiders not only the static characteristics of time-series data, butalso the time characteristics of the data.

3. The distributionally robust optimization model is configured toperform the capacity configuration of energy storage, which takes theuncertainties of the photovoltaic power, allowing the capacityconfiguration of energy storage more accurate, lowering the constructioncost of the microgrid, and enhancing the security performance of themicrogrid during operation.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to make the technical solutions of this disclosure clearer,this disclosure will be described in detail below with reference to theaccompanying drawings and embodiments. Obviously, it should be notedthat the embodiments described blow are merely some embodiments of thisdisclosure. It should be understood for those of ordinary skill in theart that other accompanying drawings can also be obtained by thefollowing accompanying drawings without paying any creative efforts.

FIG. 1 is a flowchart of a capacity configuration method of energystorage in a microgrid according to an embodiment of this disclosure;and

FIG. 2 is a flowchart of a capacity configuration system of energystorage in a microgrid according to an embodiment of this disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

In order to make the objectives, technical solutions and beneficialeffects in the embodiments of this disclosure more clear and complete,this disclosure will be described in detail below with reference to theaccompanying drawings. Obviously, the embodiments described blow aremerely some embodiments of this disclosure. Based on the embodiments ofthis disclosure, it should be understood that any modifications andreplacements made by those skilled in the art without departing from thespirit of this disclosure should fall within the scope of thisapplication defined by the appended claims.

An objective of this application is to provide a capacity configurationmethod and system of energy storage in a microgrid to overcome theinaccurate capacity configuration of energy storage in the newly-builtmicrogrids, so as to realize the capacity configuration of the energystorage equipment in the newly-built microgrid, when the data isinsufficient.

The technical solutions in the embodiments of this disclosure areprovided to solve the above-mentioned technical problems, and thegeneral idea is as follows:

In order to perform capacity configuration of the energy storageequipment in the newly-built microgrid with insufficient data, in thisdisclosure, time-series data related to photovoltaic power generation isacquired, and then preprocessed to obtain preprocessed time-series data;a time-series generative adversarial network (Time GAN) is trained basedon the preprocessed time-series data to perform data enhancement toobtain enhanced time-series data; and based on the enhanced time-seriesdata, a distributionally robust optimization model is used to performcapacity configuration of energy storage. In this disclosure, thecapacity configuration of energy storage in the newly-built microgrid isallowed to be more accurate, even though the data of the newly-builtmicrogrid is seriously insufficient, thereby improving the utilizationefficiency of the data and the construction efficiency of the microgrid.

In order to better understand the above-mentioned technical solutions,this disclosure will be described in detail below with reference to theaccompanying drawings and embodiments.

Embodiment 1

In a first aspect, provided herein is a capacity configuration method ofenergy storage in a microgrid. The method is performed as follows.

(S1) Time-series data related to photovoltaic power generation isacquired, and preprocessing of the time-series data related to thephotovoltaic power generation is performed to obtain preprocessedtime-series data.

(S2) A time-series generative adversarial network (Time GAN) is trainedbased on the preprocessed time-series data to perform data enhancementto obtain enhanced time-series data. The Time GAN includes an embeddednetwork and a generative adversarial network (GAN).

(S3) Based on the enhanced time-series data, a distributionally robustoptimization model is used to perform capacity configuration of energystorage.

In this embodiment, time-series data related to photovoltaic powergeneration is acquired, and then preprocessed to obtain preprocessedtime-series data; a time-series generative adversarial network (TimeGAN) is trained based on the preprocessed time-series data to performdata enhancement to obtain enhanced time-series data; and based on theenhanced time-series data, a distributionally robust optimization modelis used to perform capacity configuration of energy storage. The TimeGAN provided herein enables data enhancement of the limited dataacquired by the newly-built microgrid, allowing the newly-builtmicrogrid to make full use of the distributionally robust optimizationmodel to perform accurate capacity configuration of the energy storage,even though the data of the newly-built microgrid is severelyinsufficient, thereby improving the utilization efficiency of the dataand the construction efficiency of the microgrid.

Referring to an embodiment shown in FIG. 1 and the explanation of thesteps of (S1)-(S3), the embodiment is specifically performed as follows.

(S1) Time-series data related to photovoltaic power generation isacquired, and preprocessing of the time-series data related to thephotovoltaic power generation is performed to obtain preprocessedtime-series data.

The environmental data such as photovoltaic power generation power data,global horizontal radiation and diffuse horizontal radiation data,temperature data, humidity data, etc. is acquired by sensors andphotovoltaic panels during a certain period of time according to acertain collection frequency. The acquired environmental data is thetime-series data related to photovoltaic power generation. The acquiredenvironmental data is uploaded to a data acquisition module followed bypreprocessing. The preprocessing includes data cleaning processing, dataintegration processing, data transformation processing, data reductionprocessing, and data standardization processing.

(S2) A time-series generative adversarial network (Time GAN) is trainedbased on the preprocessed time-series data to perform data enhancementto obtain enhanced time-series data. The Time GAN includes an embeddednetwork and a generative adversarial network (GAN).

The time GAN is trained by a neural network training module. Thetime-series data related to photovoltaic power generation is allowed tobe regarded as a sample space containing a static feature s and atemporal feature x, whose distribution is p(S, X_(1:T)), where T is alength of the data. The network training is performed as follows.

(Step 1) The embedded network (i.e., the autoencoder network) istrained. The static feature s is mapped to a lower dimension byembedding the function in the autoencoder, which can be expressed ash_(s)=e_(S)(S), and then the temporal feature x is mapped to a lowerdimension. What is different is that a temporal feature will be relatedto a previous temporal feature h_(t-1) due to a static feature h_(s),and thus is expressed as h_(t)=e_(x)(h_(s), h_(t-1),x_(t)). It is easierfor the autoencoder network to learn the data to obtain the features viadimensionality reduction of the data. Then, the static feature h_(s),and the temporal feature h_(t) are respectively inversely mapped intothe original static feature and temporal feature via a restorationfunction. An inverse mapping function of the static feature is expressedas s=r_(S)(h_(s)), and an inverse mapping function of the static featureis expressed as x=r_(x)(h_(t)), such that a loss function of theautoencoder network is obtained and expressed as follows:

Loss_(R) =E _(S,X) _(1,T˜p) [||s−s|| ₂+Σ_(t) ||X _(t) −X _(t)||₂]  (1);

(Step 2) The GAN is trained, which is specifically performed by traininga generator and a discriminator of the GAN. The autoencoder network istrained, and then the generator and the discriminator of the GAN aretrained. Based on the static feature s of the time-series data, thegenerator is allowed to generate a static feature vector

via a first function which is expressed as:

=g_(S)(Z_(S)), which selects noise input obeys Gaussian distribution.Based on the temporal feature x of the time-series data and the staticfeature vector

, the generator is allowed to generate a temporal feature vector

via a second function, which is expressed as:

=g_(X)(

,

, z_(t)), which selects noise input of Wiener Process noise. Thediscriminator judges the difference between the data generated by thegenerator and the real data through the following two functions, whichare expressed as {tilde over (y)}_(S)=d_(S)(h_(S)); and {tilde over(y)}_(t)=d_(S)(

,

). In short, the discriminator is a binary classification neuralnetwork. Therefore, in this stage of training, two loss functions areincluded. One of the two loss functions is used to reflect theadversarial interaction between the response generator and thediscriminator, which is exhibited as follows:

Loss_(U) =E _(S,X) _(1,T˜p) [log y _(S)+Σ_(t) log y _(t) ]+E _(S,X)_(1,T˜{circumflex over (p)}) [log (b 1−

)+Σ_(t) log (1−

)]  (2);

The other loss function of the two loss functions is used to reflect theapproximation level between the data generated by the generator and thedata encoded by the autoencoder, which is exhibited as follows:

Loss_(S) =E _(S,X) _(1,T˜p) [Σ_(t) ||h _(t) −g _(X)(h _(S) ,h _(t-1) ,z_(t))||₂]  (3);

(Step 3) The embedded network and the GAN are subjected to ajoint-training, and then large number of photovoltaic power generationscenarios similar to the real scenario are output, such that the dataenhancement is conducted on the time-series data related to thephotovoltaic power generation.

(S3) Based on the enhanced time-series data, a distributionally robustoptimization model is used to perform capacity configuration of energystorage.

An objective function of the capacity configuration optimization of theenergy storage during this stage in the microgrid is determined. In thisembodiment, the lowest total cost within the project period is taken asthe optimization objective. The objective function is specifically shownas follows:

$\begin{matrix}{{C = {C_{1} + C_{2}}};} & (5)\end{matrix}$ $\begin{matrix}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};} & (6)\end{matrix}$ $\begin{matrix}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};} & (7)\end{matrix}$ $\begin{matrix}{{C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};} & (8)\end{matrix}$

where C is the total investment cost of energy storage; C₁ indicates thedaily average investment cost of energy storage battery; C₂ indicatesthe daily operating cost; r_(e) indicates the fund recovery factor; C′₁indicates the investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesthe depth of discharge of the energy storage battery; d indicates thediscount rate; y indicates the investment life of the energy storagebattery; p_(g,t) indicates the price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesthe price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate the charging and discharging power of energy storage at momentt, respectively; C_(ess) indicates the cost per unit charge anddischarge of the energy storage battery.

The worst-case scenario of the uncertainty of the photovoltaic powergeneration is described via the distributionally robust optimization,and then the Wasserstein distance is taken as an ambiguity set M^(ε)used for measuring the uncertainty of the distributionally robustoptimization model. The uncertainty model of the energy storageoperation is expressed as follows:

M ^(ε)={P_({circumflex over (p)}) ∈ M(ξ):d _(W)(P _({tilde over (p)}) ,{circumflex over (P)})≤ε}  (9);

where P_({circumflex over (p)}) is the probability distribution of theactual output power of the photovoltaic power generation; {circumflexover (P)} is the empirical distribution of the photovoltaic powergeneration; M(ξ) is all probability distribution spaces defined byWasserstein distance d_(W); ε is radius of the ambiguity set W.

During the operation of the microgrid, all equipment in the microgridare required to follow the constraints thereof, and the constraints areexpressed as follows. a. An operation constraint of the energy storageequipment is expressed as follows:

$\begin{matrix}\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right. & (10)\end{matrix}$

where SOC_(t) is the capacity of a battery at time t; φ is theself-discharging rate of the battery; η is the charging efficiency ofthe battery; p_(t) ^(c) is the charging power of the battery at time t;p_(t) ^(d) is the discharging power of the battery at time t; p_(min)^(c) is the minimum charging power of the battery; p_(max) ^(c) is themaximum charging power of the battery; p_(min) ^(d) is the minimumdischarging power of the battery; p_(max) ^(d) is the maximumdischarging power of the battery; and Δt is dispatching time interval.

b. An operation constraint of a gas turbine is expressed as follows:

$\begin{matrix}\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leq P_{{gt},t} \leq P_{gt}^{\max}}\end{matrix};} \right. & (11)\end{matrix}$

where V_(chp,t) is the amount of natural gas purchased at time t; ω isthe electrical efficiency of the gas turbine; J is the heat value of thenatural gas; H_(gt,t) is the thermal power output by the gas turbine attime t; P_(gt) ^(min) is the upper limit of electrical power (KW) of thegas turbine; and p_(gt) ^(max) is the lower limit of the electricalpower (KW) of the gas turbine.

c. A constraint of power balance is expressed as follows:

$\begin{matrix}\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right. & (12)\end{matrix}$

where P_(grid,t) represents the power transmission between the microgridand the main grid at time t; P_(pv,t) is the power output of thephotovoltaic power generation at time t; p_(t) ^(c) is the chargingpower of the energy storage battery at time t; p_(t) ^(d) is thedischarging power of the of the energy storage battery at time t,P_(load,t) is the total electrical load demand of the microgrid at timet; H_(gt,t) is the thermal power output of the gas turbine at time t;and H_(load,t) is the total heat load demand of the microgrid at time t.

In conclusion, the distributionally robust optimization model of thecapacity configuration of the energy storage is expressed as follows:

$\left\{ \begin{matrix}{{\min C} = {C_{1} + C_{2}}} \\{{s.t.{{Equations}\ \left( {10} \right)}}\  - (12)} \\{M^{\varepsilon} = \left\{ {P_{\hat{p}} \in {{{M(\xi)}:{d_{W}\left( {P_{\hat{p}},\hat{P}} \right)}} \leq \varepsilon}} \right\}}\end{matrix} \right.$

The distributionally robust optimization model of the capacityconfiguration of the energy storage is solved via a mature commercialsolver, so as to obtain the capacity configuration strategy of theenergy storage in the microgrid. The mature commercial solvers includeGurobi Optimizer and CPLEX Optimizer.

So far, the whole process of the capacity configuration of the energystorage in the microgrid of this disclosure is completed.

Embodiment 2

In a second aspect, provided herein is a capacity configuration systemof energy storage in a microgrid. The system includes a data acquisitionand preprocessing module, a time-series generative adversarial network(Time GAN) photovoltaic power generation scenario generation module andan energy storage capacity optimization and output module.

The data acquisition and preprocessing module is configured to acquiretime-series data related to photovoltaic power generation and preprocessthe time-series data related to the photovoltaic power generation toobtain preprocessed time-series data;

The time-series generative adversarial network (Time GAN) photovoltaicpower generation scenario generation module is configured to train theTime GAN to perform data enhancement based on the preprocessedtime-series data to obtain enhanced time-series data. The Time GANincludes an embedded network and a generative adversarial network (GAN).

The energy storage capacity optimization and output module is configuredto use the distributionally robust optimization model to performcapacity configuration of energy storage based on the enhancedtime-series data.

In this embodiment, the time-series data related to the photovoltaicpower generation includes photovoltaic power data, global horizontalradiation and diffuse horizontal radiation data, temperature data, andhumidity data. The preprocessing includes data cleaning processing, dataintegration processing, data transformation processing, data reductionprocessing, and data standardization processing.

In this embodiment, the time-series generative adversarial network (TimeGAN) photovoltaic power generation scenario generation module isconfigured to train the Time GAN to perform data enhancement based onthe preprocessed time-series data to obtain enhanced time-series data;the Time GAN includes an embedded network and a generative adversarialnetwork (GAN); the time-series generative adversarial network (Time GAN)photovoltaic power generation scenario generation module is operatedthrough steps of:

(S21) training the embedded network based on the time-series data; andthe embedded network is formed by embedding a function used fordimensionality reduction of the time-series data into an autoencoder;

(S22) training a generator and a discriminator in the GAN based on thetime-series data; and

(S23) performing the data enhancement on the time-series data viajoint-training of the embedded network and the GAN.

In this embodiment, the distributionally robust optimization model isshown as follows.

An objective function of the distributionally robust optimization modelis expressed as follows:

${{C = {C_{1} + C_{2}}};}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};}$${C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};$

where C is the total investment cost of energy storage; C₁ indicates thedaily average investment cost of energy storage battery; C₂ indicatesthe daily operating cost; r_(e) indicates the fund recovery factor; C′₁indicates the investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesthe depth of discharge of the energy storage battery; d indicates thediscount rate; y indicates the investment life of the energy storagebattery; p_(g,t) indicates the price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesthe price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate the charging and discharging power of energy storage at momentt, respectively; C_(ess) indicates the cost per unit charge anddischarge of the energy storage battery.

Constraints of the objective function are shown as follows.

a. An operation constraint of energy storage equipment is expressed asfollows:

$\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right.$

where SOC_(t) is the capacity of the battery at time t; φ is theself-discharging rate of the battery; η is the charging efficiency ofthe battery; p_(t) ^(c) is the charging power of the battery at time t;p_(t) ^(d) is the discharging power of the battery at time t; o_(min)^(c) is the minimum charging power of the battery; p_(max) ^(c) is themaximum charging power of the battery; p_(min) ^(d) is the minimumdischarging power of the battery; p_(max) ^(d) is the maximumdischarging power of the battery; and Δt is dispatching time interval.

b. an operation constraint of a gas turbine is expressed as follows:

$\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leqslant P_{{gt},t} \leqslant P_{gt}^{\max}}\end{matrix};} \right.$

where V_(chp,t) is the amount of natural gas purchased at time t; ω isthe electrical efficiency of the gas turbine; J is the heat value of thenatural gas; H_(gt,t) is the thermal power output by the gas turbine attime t; P_(gt) ^(min) is the upper limit of electrical power of the gasturbine; and P_(gt) ^(max) is the lower limit of the electrical power ofthe gas turbine.

c. A constraint of power balance is expressed as follows:

$\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right.$

where P_(grid,t) represents the power transmission between the microgridand the main grid at time t; P_(pv,t) is the power output of thephotovoltaic power generation at time t; p_(t) ^(c) is the chargingpower of the energy storage battery at time t; p_(t) ^(d) is thedischarging power of the of the energy storage battery at time t,P_(load,t) is the total electrical load demand of the microgrid at timet; H_(gt,t) is the thermal power output of the gas turbine at time t;and H_(load,t) is the total heat load demand of the microgrid at time t.

An ambiguity set M^(ε) used for measuring an uncertainty of thedistributionally robust optimization model is shown as follows:

M ^(ε) ={P _({circumflex over (p)}) ∈ M(ξ):d _(S)(P _({tilde over (p)}), {circumflex over (P)})≤ε};

where P_({circumflex over (p)}) is the probability distribution of theactual output power of the photovoltaic power generation; {circumflexover (P)} is the empirical distribution of the photovoltaic powergeneration; M(ξ) is all probability distribution spaces defined byWasserstein distance d_(W); ε is radius of the ambiguity set W.

In an embodiment, the distributionally robust optimization model isconfigured to perform the capacity configuration of energy storage,which is operated through a step of:

solving the distributionally robust optimization model by a commercialsolver to obtain a solution result; and performing the capacityconfiguration of energy storage based on the solution result.

Compared with the prior art, this application has the followingbeneficial effects. 1. With regard to the method and the system providedherein, the time-series data related to photovoltaic power generation isacquired and processed to obtain the preprocessed time-series data; atime-series generative adversarial network (Time GAN) is trained basedon the preprocessed time-series data to perform data enhancement toobtain enhanced time-series data; and based on the enhanced time-seriesdata, a distributionally robust optimization model is used to performcapacity configuration of energy storage. The Time GAN is configured toperform data enhancement on the limited data obtained by the newly-builtmicrogrid, so as to allow the capacity of energy storage of thenewly-built microgrid to be accurately configured using thedistributionally robust optimization model when the data is severelyinsufficient, improving the utilization efficiency of the data and theconstruction efficiency of the microgrid.

2. Compared with ordinary generative adversarial network (GAN), the TimeGAN provided herein is configured to perform data enhancement, whichconsiders not only the static characteristics of time-series data, butalso the time characteristics of the data.

3. The distributionally robust optimization model is configured toperform the capacity configuration of energy storage, which takes theuncertainties of the photovoltaic power, allowing the capacityconfiguration of energy storage more accurate, lowering the constructioncost of the microgrid, and enhancing the security performance of themicrogrid during operation.

It should be noted that as used herein, relational terms such as “first”and “second” are merely intended to distinguish one entity or operationfrom another entity or operation, and do not necessarily require orimply such an actual relationship or order between these entities oroperations. Furthermore, the term “comprise”, “include”, “contain” orany other variations are intended to encompass a non-exclusiveinclusion, such that a process, method, article, or instrument not onlyincludes those listed elements, but also includes those that are notclearly listed, or those elements that are inherent to such a process,method, article, or instrument. If there are no more restrictions, theelements defined by the sentence “comprising . . . ” do not exclude theexistence of other identical elements in the process, method, article,or instrument comprising the elements.

Described above are merely described to illustrate the technicalsolutions of this disclosure, but not intended to limit this disclosure.It should be understood for those of ordinary skill in the art that anymodifications of the technical solutions described in the aboveembodiments or the equivalent replacement of the part of the technicalfeatures can be made without departing from the spirit of theapplication should still fall within the scope of the presentapplication defined by the appended claims.

What is claimed is:
 1. A capacity configuration method of energy storagein a microgrid, comprising: acquiring time-series data related tophotovoltaic power generation, and performing preprocessing of thetime-series data related to the photovoltaic power generation to obtainpreprocessed time-series data; training a time-series generativeadversarial network (Time GAN) based on the preprocessed time-seriesdata to perform data enhancement to obtain enhanced time-series data;wherein the Time GAN comprises an embedded network and a generativeadversarial network (GAN); and based on the enhanced time-series data,performing capacity configuration of energy storage by using adistributionally robust optimization model.
 2. The capacityconfiguration method of claim 1, wherein the time-series data related tothe photovoltaic power generation comprises photovoltaic power data,global horizontal radiation and diffuse horizontal radiation data,temperature data, and humidity data; the preprocessing comprises datacleaning processing, data integration processing, data transformationprocessing, data reduction processing, and data standardizationprocessing.
 3. The capacity configuration method of claim 2, wherein theTime GAN comprises an embedded network and a generative adversarialnetwork (GAN); and the step of “training a time-series generativeadversarial network (Time GAN) based on the preprocessed time-seriesdata to perform data enhancement to obtain enhanced time-series data”comprises: (S21) training the embedded network based on the time-seriesdata; and the embedded network is formed by embedding a function usedfor dimensionality reduction of the time-series data into anautoencoder; (S22) training a generator and a discriminator in the GANbased on the time-series data; and (S23) performing the data enhancementon the time-series data via joint-training of the embedded network andthe GAN.
 4. The capacity configuration method of claim 1, wherein thedistributionally robust optimization model comprises: an objectivefunction of the distributionally robust optimization model is expressedas follows:${{C = {C_{1} + C_{2}}};}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};}$${C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};$wherein C is a total investment cost of energy storage; C₁ indicates adaily average investment cost of energy storage battery; C₂ indicates adaily operating cost; r_(e) indicates a fund recovery factor; C₁indicates an investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesa depth of discharge of the energy storage battery; d indicates adiscount rate; y indicates an investment life of the energy storagebattery; p_(g,t) indicates a price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesa price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate charging and discharging power of energy storage at moment t,respectively; and C_(ess) indicates a cost per unit charge and dischargeof the energy storage battery; constraints of the objective functioncomprise: a. an operation constraint of energy storage equipment isexpressed as follows: $\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right.$ wherein SOC_(t) is a capacity of a battery attime t; φ is a self-discharging rate of the battery; η is a chargingefficiency of the battery; p_(t) ^(c) is a charging power of the batteryat time t; p_(t) ^(d) is a discharging power of the battery at time t;p_(min) ^(c) is a minimum charging power of the battery; p_(max) ^(c) isa maximum charging power of the battery; p_(min) ^(d) is a minimumdischarging power of the battery; p_(max) ^(d) is a maximum dischargingpower of the battery; and δt is dispatching time interval; b. anoperation constraint of a gas turbine is expressed as follows:$\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leqslant P_{{gt},t} \leqslant P_{gt}^{\max}}\end{matrix};} \right.$ wherein V_(chp,t) is the amount of natural gaspurchased at time t; ω is an electrical efficiency of the gas turbine; Jis a heat value of the natural gas; H_(gt,t) is a thermal power outputby the gas turbine at time t; P_(gt) ^(min) is an upper limit ofelectrical power of the gas turbine; and P_(gt) ^(max) is a lower limitof the electrical power of the gas turbine; c. a constraint of powerbalance is expressed as follows: $\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right.$ wherein P_(grid,t) represents the powertransmission between the microgrid and a main grid at time t; P_(pv,t)is a power output of the photovoltaic power generation at time t; p_(t)^(cc) is a charging power of an energy storage battery at time t; p_(t)^(d) is a discharging power of the of the energy storage battery at timet, P_(load,t) is a total electrical load demand of the microgrid at timet; H_(gt,t) is a thermal power output of the gas turbine at time t; andH_(load,t) is a total heat load demand of the microgrid at time t; andan ambiguity set M²⁴⁹ used for measuring an uncertainty of thedistributionally robust optimization model is shown as follows:M ^(ε) ={P _({circumflex over (p)}) ∈ M(ξ):d _(W)(P _({tilde over (p)}), {circumflex over (P)})≤ε}; wherein P_({circumflex over (p)}) is aprobability distribution of an actual output power of the photovoltaicpower generation; {circumflex over (P)} is an empirical distribution ofthe photovoltaic power generation; M(ξ) is all probability distributionspaces defined by Wasserstein distance d_(W); and ε is radius of anambiguity set W.
 5. The capacity configuration method of claim 4,wherein the step of “using a distributionally robust optimization modelto perform capacity configuration of energy storage” comprises: solvingthe distributionally robust optimization model by a commercial solver toobtain a solution result; and performing the capacity configuration ofenergy storage based on the solution result.
 6. A capacity configurationsystem of energy storage in a microgrid, comprising: a data acquisitionand preprocessing module; a time-series generative adversarial network(Time GAN) photovoltaic power generation scenario generation module; andan energy storage capacity optimization and output module; wherein thedata acquisition and preprocessing module is configured to acquiretime-series data related to photovoltaic power generation and preprocessthe time-series data related to the photovoltaic power generation toobtain preprocessed time-series data; the time-series generativeadversarial network (Time GAN) photovoltaic power generation scenariogeneration module is configured to train the Time GAN to perform dataenhancement based on the preprocessed time-series data to obtainenhanced time-series data; the Time GAN comprises an embedded networkand a generative adversarial network (GAN); and the energy storagecapacity optimization and output module is configured to use thedistributionally robust optimization model to perform capacityconfiguration of energy storage based on the enhanced time-series data.7. The capacity configuration system of claim 6, wherein the time-seriesdata related to the photovoltaic power generation comprises photovoltaicpower data, global horizontal radiation and diffuse horizontal radiationdata, temperature data, and humidity data; the preprocessing comprisesdata cleaning processing, data integration processing, datatransformation processing, data reduction processing, and datastandardization processing.
 8. The capacity configuration system ofclaim 7, wherein the time-series generative adversarial network (TimeGAN) photovoltaic power generation scenario generation module isconfigured to train the Time GAN to perform data enhancement based onthe preprocessed time-series data to obtain enhanced time-series data;the Time GAN comprises an embedded network and a generative adversarialnetwork (GAN); the time-series generative adversarial network (Time GAN)photovoltaic power generation scenario generation module is operatedthrough steps of: (S21) training the embedded network based on thetime-series data; and the embedded network is formed by embedding afunction used for dimensionality reduction of the time-series data intoan autoencoder; (S22) training a generator and a discriminator in theGAN based on the time-series data; and (S23) performing the dataenhancement on the time-series data via joint-training of the embeddednetwork and the GAN.
 9. The capacity configuration system of claim 6,wherein the distributionally robust optimization model comprises: anobjective function of the distributionally robust optimization model isexpressed as follows:${{C = {C_{1} + C_{2}}};}{{C_{1} = {r_{e} \times C_{1}^{\prime} \times \frac{S}{D}}};}{{r_{e} = \frac{{d\left( {1 + d} \right)}^{y}}{365 \times \left\lbrack {\left( {1 + d} \right)^{y} - 1} \right\rbrack}};}$${C_{2} = {{p_{g,t} \times {\sum_{t = 1}^{T}\left( V_{{chp},t} \right)}} + {p_{t} \times {\sum_{t = 1}^{T}\left( P_{{grid},t} \right)}} + \left\lbrack {\sum_{t = 1}^{24}{\left( {{❘p_{t}^{c}❘} + {❘p_{t}^{d}❘}} \right)\Delta t \times C_{ess}}} \right\rbrack}};$wherein C is a total investment cost of energy storage; C₁ indicates adaily average investment cost of energy storage battery; C₂ indicates adaily operating cost; r_(e) indicates a fund recovery factor; C′₁indicates an investment cost per unit capacity of energy storagebattery; S indicates energy storage configuration capacity; D indicatesa depth of discharge of the energy storage battery; d indicates adiscount rate; y indicates an investment life of the energy storagebattery; P_(g,t) indicates a price of natural gas at time t; V_(chp,t)indicates the amount of natural gas purchased at time t; p_(t) indicatesa price of purchased electricity at time t; P_(grid,t) indicates theamount of electricity purchased at moment t; p_(t) ^(c) and p_(t) ^(d)indicate charging and discharging power of energy storage at moment t,respectively; and C_(ess) indicates a cost per unit charge and dischargeof the energy storage battery; constraints of the objective functioncomprise: a. an operation constraint of energy storage equipment isexpressed as follows: $\left\{ {\begin{matrix}{{SOC}_{t + 1} = {{{SOC}_{t}\left( {1 - \varphi} \right)} + {\left( {{p_{t}^{c} \cdot \eta} - \frac{p_{t}^{d}}{1 - \eta}} \right)\Delta t}}} \\{p_{\min}^{c} \leq p_{t}^{c} \leq p_{\max}^{c}} \\{p_{\min}^{d} \leq p_{t}^{d} \leq p_{\max}^{d}} \\{{SOC}_{t,\min} \leq {SOC}_{t} \leq {SOC}_{t,\max}}\end{matrix};} \right.$ wherein SOC_(t) is a capacity of a battery attime t; φ is a self-discharging rate of the battery; η is a chargingefficiency of the battery; p_(t) ^(c) is a charging power of the batteryat time t; p_(t) ^(d) is a discharging power of the battery at time t,p_(min) ^(c) is a minimum charging power of the battery; p_(max) ^(c) isa maximum charging power of the battery; p_(min) ^(d) is a minimumdischarging power of the battery; p_(max) ^(d) is a maximum dischargingpower of the battery; and Δt is dispatching time interval; b. anoperation constraint of a gas turbine is expressed as follows:$\left\{ {\begin{matrix}{P_{{gt},t} = {V_{{chp},t} \times J \times \omega}} \\{H_{{gt},t} = {V_{{chp},t} \times J \times \left( {1 - \omega} \right)}} \\{P_{gt}^{\min} \leqslant P_{{gt},t} \leqslant P_{gt}^{\max}}\end{matrix};} \right.$ wherein V_(chp,t) is the amount of natural gaspurchased at time t; ω is an electrical efficiency of the gas turbine; Jis a heat value of the natural gas; H_(gt,t) is a thermal power outputby the gas turbine at time t; P_(gt) ^(min) is an upper limit ofelectrical power of the gas turbine; and P_(gt) ^(max) is a lower limitof the electrical power of the gas turbine; and c. a constraint of powerbalance is expressed as follows: $\left\{ {\begin{matrix}{{P_{{grid},t} + P_{{pv},t} + p_{t}^{d} - p_{t}^{c} + P_{{gt},t}} = P_{{load},t}} \\{H_{{gt},t} = H_{{load},t}}\end{matrix};} \right.$ wherein P_(grid,t) represents the powertransmission between the microgrid and a main grid at time t; P_(pv,t)is a power output of the photovoltaic power generation at time t; p_(t)^(c) is a charging power of an energy storage battery at time t; p_(t)^(d) is a discharging power of the of the energy storage battery at timet, P_(load,t) is a total electrical load demand of the microgrid at timet; H_(gt,t) is a thermal power output of the gas turbine at time t; andH_(load,t) is a total heat load demand of the microgrid at time t; andan ambiguity set M^(ε) used for measuring an uncertainty of thedistributionally robust optimization model is shown as follows:M ^(ε) ={P _({circumflex over (p)}) ∈ M(ξ);d _(W)(P _({tilde over (p)}), {circumflex over (P)})≤ε}; wherein P_({circumflex over (p)}) is aprobability distribution of an actual output power of the photovoltaicpower generation; {circumflex over (P)} is an empirical distribution ofthe photovoltaic power generation; M(ξ) is all probability distributionspaces defined by Wasserstein distance d_(W); and ε is radius of anambiguity set W.
 10. The capacity configuration system of claim 9,wherein the distributionally robust optimization model is configured toperform the capacity configuration of energy storage; which is operatedthrough a step of: solving the distributionally robust optimizationmodel by a commercial solver to obtain a solution result; and performingthe capacity configuration of energy storage based on the solutionresult.